An in-silico exploration of an evolutionarily convergent TGF-𝛽 mimetic from a parasitic nematode Heligmosomoides polygyrus bakeri

Background

Previous work has shown that an intestinal helminth parasite (Heligmosomoides polygyrus) is able to expand host regulatory T-cell populations through activation of TGF-𝛽 receptors(Johnston et al. 2017). Further work went on to identify a protein, H. polygyrus TGF-β mimic (Hp-TGM), which potently binds host TGF-𝛽 receptors, while containing no sequence-level homology to host TGF-𝛽.

Objectives

In this notebook, we will compare human TGF-𝛽 to the H. polygyrus TGF-β mimic (Hp-TGM) in-silico using a multi-layered strategy. First, we will compare amino acid sequences between the two proteins searching for both local and global homology. Second, we will fold these two proteins using ESM2 and compare structural motifs. Finally we will employ molecular docking between the two proteins and the human TGF-𝛽 receptor (____). These analyses will help us determine the comparative value of sequence, structural, and molecular docking based computational approaches when comparing evolutionary convergent protein-receptor binding pairs.

Analysis

Setup

Code

library(tidyverse)
library(reticulate)
library(magrittr)
library(glue)
library(seqinr)
library(future)
library(batchtools)
library(future.batchtools)
library(fs)
library(tictoc)
library(listenv)
library(progress)
library(viridis)
library(bio3d)
library(r3dmol)
library(strex)
library(plotly)
library(ggsci)

tmpdir <- "/central/scratch/jbok/tmp"
homedir <- "/central/groups/MazmanianLab/joeB"
wkdir <- glue(
  "{homedir}/",
  "Microbiota-Immunomodulation/Microbiota-Immunomodulation"
)
src_dir <- glue("{wkdir}/notebooks")
source(glue("{src_dir}/R-scripts/helpers_general.R"))
source(glue("{src_dir}/R-scripts/helpers_sequence-screens.R"))
source(glue("{src_dir}/R-scripts/rhmmer-package.R"))
protein_catalogs <- glue("{homedir}/", "Downloads/protein_catalogs")
shell_do(glue("mkdir -p {wkdir}/data/interim/fastas/raw/monomers"))
shell_do(glue("mkdir -p {wkdir}/data/interim/fastas/processed/monomers"))
shell_do(glue("mkdir -p {wkdir}/data/interim/fastas/processed/complexes"))

Code

gget_seq <- function(ensembl_id, amino_acid = TRUE) {
  seq_results <- gget$seq(ensembl_id, translate = amino_acid)
  return(seq_results[2])
}
tgf_keys <- c("TGFB1", "TGFB2", "TGFB3", "TGFBR1", "TGFBR2", "TGFBR3")
tgfb_search <- gget$search(tgf_keys, "homo_sapiens")
tgfb_search %<>% filter(biotype == "protein_coding", gene_name %in% tgf_keys)
# collect amino acid sequences for each protein using the ensembl_id
tgfb_search %<>% mutate(sequences_aa =  map_chr(ensembl_id, gget_seq))

HP_TMG_SEQ <- "DDSGCMPFSDEAATYKYVAKGPKNIEIPAQIDNSGMYPDYTHVKRFCKGLHGEDTTGWFVGICLASQWYYYEGVQECDDRRCSPLPTNDTVSFEYLKATVNPGIIFNITVHPDASGKYPELTYIKRICKNFPTDSNVQGHIIGMCYNAEWQFSSTPTCPASGCPPLPDDGIVFYEYYGYAGDRHTVGPVVTKDSSGNYPSPTHARRRCRALSQEADPGEFVAICYKSGTTGESHWEYYKNIGKCPDPRCKPLEANESVHYEYFTMTNETDKKKGPPAKVGKSGKYPEHTCVKKVCSKWPYTCSTGGPIFGECIGATWNFTALMECINARGCSSDDLFDKLGFEKVIVRKGEGSDSYKDDFARFYATGSKVIAECGGKTVRLECSNGEWHEPGTKTVHRCTKDGIRTL"
TGFB1 <- "ALDTNYCFSSTEKNCCVRQLYIDFRKDLGWKWIHEPKGYHANFCLGPCPYIWSLDTQYSKVLALYNQHNPGASAAPCCVPQALEPLPIVYYVGRKPKVEQLSNMIVRSCKCS"
TGFB2 <- "ALDAAYCFRNVQDNCCLRPLYIDFKRDLGWKWIHEPKGYNANFCAGACPYLWSSDTQHSRVLSLYNTINPEASASPCCVSQDLEPLTILYYIGKTPKIEQLSNMIVKSCKCS"
TGFB3 <- "ALDTNYCFRNLEENCCVRPLYIDFRQDLGWKWVHEPKGYYANFCSGPCPYLRSADTTHSTVLGLYNTLNPEASASPCCVPQDLEPLTILYYVGRTPKVEQLSNMVVKSCKCS"

hp_mimic_data <-
  tribble(
    ~gene_name, ~sequences_aa,
    "HP-TGM",   HP_TMG_SEQ
  )
tgfb_search %<>% bind_rows(hp_mimic_data)

saveRDS(tgfb_search, glue(
  "{wkdir}/data/interim/tmp/",
  "{Sys.Date()}_TGFB_gget.rds"
))

Saving individual sequences in FASTA format.

Code

# Save individual fasta files for each protein -----
tgfb_search <- readRDS(glue(
  "{wkdir}/data/interim/tmp/",
  "2023-02-27_TGFB_gget.rds"
))

# Manually editing the fasta sequences to remove the signal peptide and other non-active domains
tgfb_search %<>% 
  mutate(sequences_aa = case_when(
    gene_name == "TGFB1" ~ TGFB1,
    gene_name == "TGFB2" ~ TGFB2,
    gene_name == "TGFB3" ~ TGFB3,
    TRUE ~ sequences_aa  
  ))

fastas_raw_dir <- glue("{wkdir}/data/interim/fastas/raw/monomers")
for (tf in tgfb_search$gene_name) {
  tf_aa <- tgfb_search %>%
    filter(gene_name == tf) %>%
    pull(sequences_aa)
  fasta_path <- glue("{fastas_raw_dir}/{tf}.fasta")
  write.fasta(
    sequences = tf_aa,
    names = tf,
    file.out = fasta_path,
    open = "w",
    nbchar = 60,
    as.string = FALSE
  )
}

Applying SignalP 6.0 to predict and remove signal peptides from fasta files.

Code

fastas_raw_paths <- paste0(
  fastas_raw_dir, "/", 
  list.files(fastas_raw_dir, pattern = "fasta")
)
names(fastas_raw_paths) <- fastas_raw_paths %>% 
  purrr::map(~fs::path_ext_remove(basename(.)))

fastas_processed_tgfb <- list()
for (id in names(fastas_raw_paths)) {
  fasta_path <- fastas_raw_paths[[id]]
  fastas_processed_tgfb[[id]] <- glue(
    "{wkdir}/data/interim/fastas/processed/monomers/{id}"
    )
  if (dir.exists(fastas_processed_tgfb[[id]])) {
    next
  } 
  message("Processing ", id, " fasta file...")
  slurm_shell_do(
    glue(
      "conda run -n signalp6",
      " signalp6",
      " --organism eukarya",
      " --fastafile {fasta_path}",
      " --output_dir {fastas_processed_tgfb[[id]]}",
      " --write_procs 8",
      " --format all",
      " --mode fast"
      ),
      ncpus = 8
  )
}

# IF no signal peptide is found, the fasta file is copied to the processed dir
for (id in names(fastas_raw_paths)) {
  raw_fasta <- fastas_raw_paths[[id]]
  processed_fasta <- glue(
    "{fastas_processed_tgfb[[id]]}/",
    "processed_entries.fasta"
    )
  if (file.info(processed_fasta)$size == 0){
    message("Overwriting ", id, " processed fasta file...")
    shell_do(glue("cp {raw_fasta} {processed_fasta}"))
  }
}

Saving FASTA files for complex predictions

Code

# Saving protein complexes (post-processing) ----
tgfb_search <- readRDS(glue(
  "{wkdir}/data/interim/tmp/",
  "2023-02-27_TGFB_gget.rds"
))
# Split into TFs and Receptors and combine all possible pairs
tgf_receptors <- tgfb_search %>% 
  filter(grepl("receptor", ensembl_description)) %>% 
  pull(gene_name)
tgf_tf <- tgfb_search %>% 
  filter(!grepl("receptor", ensembl_description)) %>% 
  pull(gene_name)

format_seq <- function(fasta) {
  fasta %>% 
    seqinr::getSequence() %>% 
    unlist() %>%
    paste(collapse = "")
}

# Saving paired fasta files
fastas_merged_path <- glue("{wkdir}/data/interim/fastas/processed/complexes")
for (tf in tgf_tf) {
  tf_aa <- seqinr::read.fasta(
      glue("{fastas_processed_tgfb[[tf]]}/processed_entries.fasta"),
      seqtype = "AA"
      )
  for (receptor in tgf_receptors) {
    rec_aa <- seqinr::read.fasta(
      glue("{fastas_processed_tgfb[[receptor]]}/processed_entries.fasta"),
      seqtype = "AA"
      )
    merged_name <- paste(
      seqinr::getName(tf_aa), 
      seqinr::getName(rec_aa), 
      sep = "_"
      )
    merged_fasta_path <- glue("{fastas_merged_path}/{merged_name}.fasta")
    if (file.exists(merged_fasta_path)) {
      next
    }
    message("Saving: ", merged_name)
    write.fasta(
      sequences = list(format_seq(tf_aa), format_seq(rec_aa)),
      names =list(seqinr::getName(tf_aa), seqinr::getName(rec_aa)),
      file.out = merged_fasta_path,
      open = "w",
      nbchar = 60,
      as.string = FALSE
    )
  }
}

# Saving trioed fasta files
rec1 <- seqinr::read.fasta(
  glue("{fastas_processed_tgfb[['TGFBR1']]}/processed_entries.fasta"),
  seqtype = "AA"
  )
rec2 <- seqinr::read.fasta(
  glue("{fastas_processed_tgfb[['TGFBR2']]}/processed_entries.fasta"),
  seqtype = "AA"
  )
for (tf in tgf_tf) {
  tf_aa <- seqinr::read.fasta(
      glue("{fastas_processed_tgfb[[tf]]}/processed_entries.fasta"),
      seqtype = "AA"
      )
  merged_name <- paste(seqinr::getName(tf_aa), "TGFBR1_TGFBR2", sep = "_")
  merged_fasta_path <- glue("{fastas_merged_path}/{merged_name}.fasta")
  if (file.exists(merged_fasta_path)) {
    next
  }
  message("Saving: ", merged_name)
  write.fasta(
    sequences = list(
      format_seq(tf_aa), 
      format_seq(rec1),
      format_seq(rec2)
      ),
    names =list(
      seqinr::getName(tf_aa),
      seqinr::getName(rec1), 
      seqinr::getName(rec2)
      ),
    file.out = merged_fasta_path,
    open = "w",
    nbchar = 60,
    as.string = FALSE
  )
}

Sequence Similarity (Local & Global)

Creating a dataframe of job metadata for slurm batchtools.

Code

sequence_analysis_dir <- glue(
  "{wkdir}/data/interim/",
  "tgfb_analyses/sequence-alignment"
)
tgfb_ligand_dir <- glue("{wkdir}/data/interim/alphafold-multimer/TGFB_ligands")
shell_do(glue("mkdir -p {sequence_analysis_dir}"))
shell_do(glue("mkdir -p {tgfb_ligand_dir}"))

seqinr::write.fasta(
  sequences = as.list(tgf_tf$sequences_aa),
  names = as.list(tgf_tf$gene_name),
  file.out = glue("{tgfb_ligand_dir}/tgfb_ligands.fasta"),
  open = "w",
  nbchar = 60,
  as.string = FALSE
)

alignment_df_list <- bind_rows(
  tgf_tf %>%
    mutate(ensembl_id = gene_name) %>%
    dplyr::select(ensembl_id, sequences_aa) %>%
    mutate(method = "needle"),
  tgf_tf %>%
    mutate(ensembl_id = gene_name) %>%
    dplyr::select(ensembl_id, sequences_aa) %>%
    mutate(method = "water")
) %>%
  mutate(
    refdb_path = glue("{tgfb_ligand_dir}/tgfb_ligands.fasta"),
    output_dir = sequence_analysis_dir
  )

alignment_df_list %>% glimpse()

Executing slurm sequence alignment jobs.

Code

# configure registry ----
cluster_run <- glue("{Sys.Date()}_TGFB-EMBOSS-Alignment_ID-{rand_string()}/")
message("\n\nRUNNING:  ", cluster_run, "\n")
breg <- makeRegistry(
  file.dir = glue(
    "{wkdir}/.cluster_runs/",
    cluster_run
  ),
  seed = 42
)
breg$cluster.functions <- batchtools::makeClusterFunctionsSlurm(
  template = glue("{wkdir}/batchtools_templates/batchtools.slurm.tmpl"),
  scheduler.latency = 0.5,
  fs.latency = 65
)
# Submit Jobs ----
jobs <- batchMap(
  fun = align_fasta_sequences,
  args = alignment_df_list,
  reg = breg
)
setJobNames(jobs,
  paste("TGFB-EMBOSS",
  alignment_df_list$ensembl_id,
  alignment_df_list$method,
  sep = "_"),
  reg = breg
)
getJobNames(jobs, reg = breg)
submitJobs(jobs,
  resources = list(
    walltime = 3600,
    memory = 1024,
    ncpus = 1,
    max.concurrent.jobs = 9999
  )
)

Aggregating alignment files.

Code

file_paths <- list.files(sequence_analysis_dir, pattern = "rds$", full.names = TRUE)
needle_files <- file_paths %>%
  keep(grepl("HP-TGM_tgfb_ligands.needle.rds", .))
water_files <- file_paths %>%
  keep(grepl("HP-TGM_tgfb_ligands.water.rds", .))

tgfb_alignments <-  
  bind_rows(
    file_paths %>% 
      keep(grepl("needle", .)) %>% 
      load_rds_files() %>% 
      mutate(method = "Needleman-Wunsch (global) "),
    file_paths %>% 
      keep(grepl("water", .)) %>% 
      load_rds_files() %>% 
      mutate(method = "Smith-Waterman (local)")
  )

saveRDS(tgfb_alignments,
  glue("{wkdir}/data/interim/tgfb_analyses/sequence-alignment-df.rds")
  )

Code

tgfb_alignments <- 
  readRDS(
    glue("{wkdir}/data/interim/tgfb_analyses/sequence-alignment-df.rds")
  )

# Plotting sequence alignment data
alignment_heatmap <- tgfb_alignments %>% 
  ggplot(aes(x = protein_1, y = protein_2, fill = Score)) +
  geom_tile() +
  geom_text(aes(label = paste0(percent_identity, "%\n", percent_similarity, "%")), 
    color = "white", size = 3) +
  theme_bw() +
  facet_wrap( ~ method) +
  scale_x_discrete(expand=c(0,0)) +
  scale_y_discrete(expand=c(0,0)) +
  scale_fill_viridis_c(option = "G") +
  labs(x = "", y = "", fill = "Alignment\nScore") +
  theme(axis.text.x = element_text(angle=45, hjust=1) )

ggsave(alignment_heatmap, 
  filename = glue("{wkdir}/figures/tgfb-analysis/sequence-alignment-heatmap.png"),
  dpi = 600, width = 7, height = 3
)

Figure 1: Local and global pairwise sequence alignment.

This figure depicts pairwise sequence alignment scores between all human TGF-β isomers I-III and the H. polygyrus TGF-β mimic (Hp-TGM). Color of the heatmap represents the alignment score and the top and bottom values within each cell indicate the percent identity and similarity of a sequence pair, respectively. These results indicate that human TGF-β isomers share a moderate degree of sequence similarity within isomer comparisons, but very little similarity with HP-TGM. This is indicated by a decrease of approximately two-fold in the percentage similarity observed in inter-species comparisons.

Hidden Markov Model Similarity

Creating MSA profiles for each protein using jackhmmer against UniProt.

Code

tgfb_fasta_dir <- glue("{wkdir}/data/interim/alphafold-multimer/TGFB_fasta")
tgfb_fasta_paths <- paste0(
  tgfb_fasta_dir, "/", 
  list.files(tgfb_fasta_dir, pattern = "fasta") %>% keep(!grepl("_", .))
)

for (fasta_path in tgfb_fasta_paths) {
  id <- fs::path_ext_remove(basename(fasta_path))
  output_dir <- glue(
    "{wkdir}/data/processed/",
    "jackhmmer_results/{id}"
  )
  shell_do(glue("mkdir -p {output_dir}"))
  if (!file.exists(glue("{output_dir}/{id}_msa.fasta"))) {
    slurm_shell_do(
      cmd = glue(
        "jackhmmer ",
        " --cpu 8",
        " -N 5",
        " -E 0.05",
        " -A {output_dir}/{id}_msa.fasta",
        " -o {output_dir}/{id}_jackhmmer.out",
        " --tblout {output_dir}/{id}_seqhits.txt",
        " --domtblout {output_dir}/{id}_domainhits.txt",
        " --noali",
        " {fasta_path}",
        " /central/software/alphafold/data/uniprot/uniprot.fasta"
      ),
      jobname = glue("jkhmr_{fasta_path}_{rand_string()}"),
      working_dir = wkdir,
      ncpus = 8,
      memory = "4G",
      walltime = 36000
    )
  }
}

Creating HMM profiles from MSAs and estimating sequence similiarity using Hmmsearch on custom protein catalog.

Code

protein_catalog_path <- glue(
  "{homedir}/Downloads/protein_catalogs/clustered_catalogs",
  "/merged/2023-02-13_catalog_MMSeq2-95_rep_seq.fasta"
)
ids <- tgfb_fasta_paths %>%
  purrr::map(~ fs::path_ext_remove(basename(.)))

for (id in ids){
  input_dir <- glue(
    "{wkdir}/data/processed/",
    "jackhmmer_results/{id}"
  )
  output_dir <- glue(
    "{wkdir}/data/processed/",
    "hmmersearch_results/{id}"
  )
  shell_do(glue("mkdir -p {output_dir}"))
  # make hmm profile from MSA
  fasta_path <- glue("{input_dir}/{id}_msa.fasta")
  shell_do(glue("hmmbuild {output_dir}/{id}.hmm {fasta_path}"))
  # hmmsearch on protein catalog using hmm profile
  slurm_shell_do(
    cmd = glue(
      "hmmsearch ",
      " --cpu 8",
      " -A {output_dir}/{id}_msa.hmm",
      " -o {output_dir}/{id}_hmmsearch.out",
      " --tblout {output_dir}/{id}_tblout.txt",
      " --domtblout {output_dir}/{id}_domtblout.txt",
      " --noali",
      " {output_dir}/{id}.hmm",
      " {protein_catalog_path}"
    ),
    jobname = glue("hmrsearch_{fasta_path}_{rand_string()}"),
    working_dir = wkdir,
    ncpus = 8,
    memory = "4G",
    walltime = 36000
  )
}

Visualizing pHMM results.

Code

catalogs <- c(
  "ELGG",
  "Hadza",
  "KIJ",
  "MGV",
  "RUGS",
  "RUMMETA",
  "UHGP",
  "VEuPathDB",
  "Wormbase"
)

catalogs_cols <- pal_npg()(length(catalogs))
names(catalogs_cols) <- catalogs

hmmer_results_dir <- glue("{wkdir}/data/interim/tgfb_analyses/hmmersearch_results/")
hmmer_results_paths <- list.dirs(
  glue("{wkdir}/data/interim/tgfb_analyses/hmmersearch_results/"), 
  recursive = FALSE)

hmmer_hit_plots <- list()
for (id in basename(hmmer_results_paths)) {
  sequence_hits <- read_tblout(
  glue("{hmmer_results_dir}/{id}/{id}_tblout.txt")
  )
  hmmer_hit_plots[[id]] <- sequence_hits %>% 
    mutate(catalog = map_chr(
        str_before_nth(domain_name, "_", 2), 
        ~ str_remove(., "CATID_"))
        ) %>%
      ggplot(aes(
        x = -log10(sequence_evalue), 
        y = -log10(best_domain_evalue),
        group = description
        )) +
      scale_color_manual(values = catalogs_cols) +
      geom_point(aes(color = catalog)) +
      theme_bw()
}

HP-TGM

Code

ggplotly(hmmer_hit_plots$`HP-TGM` )

TGF-β1

Code

ggplotly(hmmer_hit_plots$TGFB1)

TGF-β2

Code

ggplotly(hmmer_hit_plots$TGFB2)

TGF-β3

Code

ggplotly(hmmer_hit_plots$TGFB3)

Structural Analysis and Complex Predictions

Code

receptor_ligand_pairs <- c(
  # "TGFB1_TGFBR1",
  # "TGFB2_TGFBR2",
  # "TGFB3_TGFBR1_TGFBR2",
  # "TGFB2_TGFBR1",
  # "TGFB2_TGFBR2",
  # "TGFB2_TGFBR1_TGFBR2",
  "TGFB3_TGFBR1",
  "TGFB3_TGFBR2",
  "TGFB3_TGFBR1_TGFBR2",
  "HP-TGM_TGFBR1",
  "HP-TGM_TGFBR2",
  "HP-TGM_TGFBR1_TGFBR2"
)

pdb_models <- list()
for (pair in receptor_ligand_pairs) {
  pdb_data <- bio3d::read.pdb(
    glue(
      "{wkdir}/data/interim/alphafold-multimer/{pair}/{pair}",
      "/ranked_0.pdb"
    ),
    multi = TRUE
  )
  
  pdb_models[[pair]] <- r3dmol(
    viewer_spec = m_viewer_spec(
      cartoonQuality = 2,
      lowerZoomLimit = 50,
      upperZoomLimit = 2000
    )
  ) %>%
    m_add_model(data = m_bio3d(pdb_data)) %>%
    m_add_surface(style = m_style_surface(opacity = 0.4)) %>%
    m_set_style(style = m_style_cartoon()) %>%
    m_set_style(sel = m_sel(chain = "B"),
                style = m_style_cartoon(color = "#FFD105")) %>%
    m_set_style(sel = m_sel(chain = "C"),
                style = m_style_cartoon(color = "#FC027E")) %>% 
    m_zoom_to() %>% 
    m_rotate(angle = 90, axis = "y") %>%
    m_spin()
  
}

[TGF-β3 - TGF-βR1]

Code

pdb_models$TGFB3_TGFBR1

[TGF-β3 - TGF-βR2]

Code

pdb_models$TGFB3_TGFBR2

[TGF-β3 - TGF-βR1 - TGF-βR2]

Code

pdb_models$TGFB3_TGFBR1_TGFBR2

[HP-TGM - TGF-βR1]

Code

pdb_models$`HP-TGM_TGFBR1`

[HP-TGM - TGF-βR2]

Code

pdb_models$`HP-TGM_TGFBR2`

[HP-TGM - TGF-βR1 - TGF-βR2]

Code

pdb_models$`HP-TGM_TGFBR1_TGFBR2`

Complex Binding Energies

Binding Interface Analysis

Methods

Signal Peptide Removal Signal Peptide Signal peptide prediction model based on a Bert protein language model encoder and a conditional random field (CRF) decoder. organism as eukarya to trigger post-processing of the SP predictions to prevent spurious results (only predicts type Sec/SPI). using fast mode

Sequence Similarity Analysis

Structural Similarity

Docking

R-Packages

Code

sessionInfo()

R version 4.2.0 (2022-04-22)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /central/home/jboktor/miniconda3/envs/pdmbsR/lib/libopenblasp-r0.3.21.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggsci_2.9                plotly_4.10.1            strex_1.4.4             
 [4] r3dmol_0.2.0             bio3d_2.4-4              viridis_0.6.2           
 [7] viridisLite_0.4.0        progress_1.2.2           listenv_0.8.0           
[10] tictoc_1.1               fs_1.5.2                 future.batchtools_0.10.0
[13] batchtools_0.9.16        future_1.26.1            seqinr_4.2-23           
[16] glue_1.6.2               magrittr_2.0.3           reticulate_1.26         
[19] forcats_0.5.1            stringr_1.4.1            dplyr_1.0.9             
[22] purrr_0.3.5              readr_2.1.2              tidyr_1.2.0             
[25] tibble_3.1.7             ggplot2_3.4.0            tidyverse_1.3.1         

loaded via a namespace (and not attached):
 [1] bit64_4.0.5       lubridate_1.8.0   httr_1.4.3        tools_4.2.0      
 [5] backports_1.4.1   utf8_1.2.2        R6_2.5.1          lazyeval_0.2.2   
 [9] DBI_1.1.2         colorspace_2.0-3  ade4_1.7-19       withr_2.5.0      
[13] tidyselect_1.1.2  gridExtra_2.3     prettyunits_1.1.1 bit_4.0.4        
[17] compiler_4.2.0    textshaping_0.3.6 cli_3.4.1         rvest_1.0.2      
[21] xml2_1.3.3        labeling_0.4.2    scales_1.2.0      checkmate_2.1.0  
[25] rappdirs_0.3.3    systemfonts_1.0.4 digest_0.6.30     rmarkdown_2.18   
[29] pkgconfig_2.0.3   htmltools_0.5.3   parallelly_1.31.1 dbplyr_2.1.1     
[33] fastmap_1.1.0     htmlwidgets_1.5.4 rlang_1.0.6       readxl_1.4.0     
[37] rstudioapi_0.13   shiny_1.7.1       farver_2.1.0      generics_0.1.2   
[41] jsonlite_1.8.3    crosstalk_1.2.0   vroom_1.5.7       Matrix_1.5-3     
[45] Rcpp_1.0.8.3      munsell_0.5.0     fansi_1.0.3       lifecycle_1.0.3  
[49] stringi_1.7.8     yaml_2.3.6        MASS_7.3-57       grid_4.2.0       
[53] promises_1.2.0.1  parallel_4.2.0    crayon_1.5.2      lattice_0.20-45  
[57] haven_2.5.0       hms_1.1.1         knitr_1.40        pillar_1.8.1     
[61] base64url_1.4     codetools_0.2-18  reprex_2.0.1      evaluate_0.18    
[65] data.table_1.14.2 modelr_0.1.8      httpuv_1.6.5      png_0.1-7        
[69] vctrs_0.5.0       tzdb_0.3.0        cellranger_1.1.0  gtable_0.3.0     
[73] assertthat_0.2.1  xfun_0.34         mime_0.12         xtable_1.8-4     
[77] broom_0.8.0       later_1.3.0       ragg_1.2.2        globals_0.15.0   
[81] ellipsis_0.3.2    brew_1.0-8       

References

Johnston, Chris J. C., Danielle J. Smyth, Ravindra B. Kodali, Madeleine P. J. White, Yvonne Harcus, Kara J. Filbey, James P. Hewitson, et al. 2017. “A Structurally Distinct TGF-β Mimic from an Intestinal Helminth Parasite Potently Induces Regulatory t Cells.” Nature Communications 8 (November): 1741. https://doi.org/10.1038/s41467-017-01886-6.